The presence of metals in a patient, such as a dental filling, a hip prosthesis, an implanted marker, applicators used in brachytherapy, and surgical chips, may cause streaking artifacts in X-ray CT and has been long been recognized as a problem that limits the applications of CT imaging. In the last three decades, many efforts have been devoted to reduce metal artifacts in CT. There are two types of techniques. One is first to identify the metal-contaminated region in the projection space and then replace them by using different interpolation schemes, such as linear interpolation, multi-resolution interpolation or variational method based on the un-contaminated projection data. CT images are then reconstructed from the completed projection data by analytical filtered back-projection (FBP)-type algorithms. In reality, identification of a metal object in the projection is not always easy, especially when the object is behind a high-density structure such as the bone. Alternatively, metal artifacts are reduced by using a model-based iterative reconstruction algorithm which are advantageous in modeling the image formation and incorporating a prior knowledge. A major shortcoming of the existing iterative algorithms is that, the knowledge about the shape and location, and sometimes even the attenuation coefficients, of the metal objects are required to ensure the effective removal of the metal artifacts. In reality, however, knowing this information before reconstruction is a very strong, and often impractical, assumption to impose, and makes iterative algorithms not useful for general clinical applications.
Because accurate localization of metals in CT images is a critical step for metal artifacts reduction in CT imaging and many practical applications of CT images, what is needed is a method capable of auto-identifying the shape and location of metallic object(s) in the image space.